GALEN - 2015 - Annual activity report

GALEN

GALEN - 2015

Project-Team Galen

Members

Overall Objectives

GALEN@Centrale-Paris

Research Program

Shape, Grouping and Recognition
Machine Learning & Structured Prediction
Self-Paced Learning with Missing Information
Discrete Biomedical Image Perception

Application Domains

Lung Tumor Detection and Characterization
Co-segmentation and Co-registration of Subcortical Brain Structures

Highlights of the Year

New Software and Platforms

DISD
DPMS
DROP
FastPD
GraPeS
HOAP-SVM
LBSD
TeXMeG
mrf-registration

New Results

Optimizing Average Precision
Region-based Semantic Segmentation
Parsimonious Labeling
Structured Learning and Inference
Novel graph kernels
Structured Sparsity Regularization & Statistical Hypothesis Testing
High-Order MRF models
Graph-based registration and segmentation
Object Detection from RGB-Depth images
Deep CNN for Modelling Deformations and Semantic Segmentation
Learning Low-level Image Representations with Deep CNN
Human-Limb Segmentation for Intelligent Mobility Assistance Robots

Partnerships and Cooperations

Regional Initiatives
National Initiatives
European Initiatives
International Initiatives
International Research Visitors

Dissemination

Promoting Scientific Activities
Teaching - Supervision - Juries
Popularization

Bibliography

Publications of the year

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Section: New Software and Platforms

DROP

Keywords: Health - Merging - Registration of 2D and 3D multimodal images - Medical imaging

Functional Description

Drop is a software programme that registers images originating from one or more modes by quickly and efficiently calculating a non-rigid / deformable field of deformation. Drop is a new, quick and effective registration tool based on new algorithms that do not require a cost function derivative.

Partner: Centrale Paris
Contact: Nikolaos Paragyios
URL: http://campar.in.tum.de/Main/Drop

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